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href="/zwyywz/2023/04/15/%E5%9C%A8ubuntu%E4%B8%8A%E4%BB%8E0%E5%BC%80%E5%A7%8B%E9%85%8D%E7%BD%AE%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83/" title="在Ubuntu上从0开始配置深度学习开发环境"><img class="post-bg" src="https://cdn.educba.com/academy/wp-content/uploads/2020/01/Deep-Learning.jpg" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="在Ubuntu上从0开始配置深度学习开发环境"></a></div><div class="recent-post-info"><a class="article-title" href="/zwyywz/2023/04/15/%E5%9C%A8ubuntu%E4%B8%8A%E4%BB%8E0%E5%BC%80%E5%A7%8B%E9%85%8D%E7%BD%AE%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83/" title="在Ubuntu上从0开始配置深度学习开发环境">在Ubuntu上从0开始配置深度学习开发环境</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2023-04-15T01:25:00.000Z" title="发表于 2023-04-15 09:25:00">2023-04-15</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-04-15T12:21:01.584Z" title="更新于 2023-04-15 20:21:01">2023-04-15</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/zwyywz/categories/%E5%BC%80%E5%8F%91%E6%95%99%E7%A8%8B/">开发教程</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/zwyywz/categories/%E5%BC%80%E5%8F%91%E6%95%99%E7%A8%8B/%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/">学习笔记</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/zwyywz/tags/DeepLearning/">DeepLearning</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83/">开发环境</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/%E7%B3%BB%E7%BB%9F%E5%AE%89%E8%A3%85/">系统安装</a></span></div><div class="content">在Ubuntu上从0开始配置深度学习开发环境1、安装ubuntu server1.1 Ventoy 启动U盘简单来说，Ventoy是一个制作可启动U盘的开源工具。有了Ventoy你就无需反复地格式化U盘，你只需要把 ISO/WIM/IMG/VHD(x)/EFI 等类型的文件直接拷贝到U盘里面就可以启动了，无需其他操作。你可以一次性拷贝很多个不同类型的镜像文件，Ventoy 会在启动时显示一个菜单来供你进行选择。详细使用请看官网：ventoy官网
 下载好后解压，解压完成后，双击运行“ventoy2disk.exe”打开它。

Ventoy 安装完成之后，U盘会被分成两个区。第一个分区将会被默认格式化为 exFAT 格式的文件系统，你可以在这里存放日常使用的普通文档，当普通 U 盘使用。当你需要制作启动盘时，你只需要把系统镜像文件 .iso 拷贝到这个分区里面即可。

关机重启，选择从U盘启动，不同主板的启动方式不一样，请自行百度。启动后就可以看到如下页面。

1.2 安装ubuntu server当开启安装Ubuntu按钮后，会短暂出现如下只显示logo的图形界面，此时可以默认不操作， ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/zwyywz/2023/01/15/zkViewYolo/" title="针对zkview平台上嵌入机器视觉算法的框架"><img class="post-bg" src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/image-20230410111343844.png" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="针对zkview平台上嵌入机器视觉算法的框架"></a></div><div class="recent-post-info"><a class="article-title" href="/zwyywz/2023/01/15/zkViewYolo/" title="针对zkview平台上嵌入机器视觉算法的框架">针对zkview平台上嵌入机器视觉算法的框架</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2023-01-15T01:25:00.000Z" title="发表于 2023-01-15 09:25:00">2023-01-15</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-04-16T13:04:25.663Z" title="更新于 2023-04-16 21:04:25">2023-04-16</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/zwyywz/categories/%E5%BC%80%E6%BA%90%E9%A1%B9%E7%9B%AE/">开源项目</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/zwyywz/tags/zkview/">zkview</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/machine-vision/">machine vision</a></span></div><div class="content">这是一个针对zkview平台上嵌入机器视觉算法的框架一、简介这是一个针对zkview工业互联平台组态软件所设计的机器视觉库，目前所嵌入的算法以YOLO为主，主要包括了YOLOv3、YOLOv5、YOLOx。用以完成目标检测任务。其识别任务的基本流程是：
摄像头输入 -&gt; 目标检测算法逐帧检测 -&gt; ffmpeg逐帧推流成FLV格式视频流 -&gt;前端展示视频
1、摄像头输入设置包括但不限于： 本地摄像头、流媒体摄像头（支持RTSP、RTMP、HTTP-flv等格式摄像头）、各种格式视频（.mp4、.mov、.avi等）
2、目标检测算法：本项目开发一套基于TensorRT库高性能YOLO推理框架，使用cuda实现了对于输入图片的批量并行预处理、实现了模型的Int8量化、实现了消费者-生产者模型、将推理速度由70+ fps 提升到 150+ fps
3、ffmpeg视频推流：目前实现了将检测图片由H264编码器编码后推流成FLV格式视频流
4、前端展示页面：zkview 专业的工业互联网WEB组态软件，共享资源，海量组件，高效开发前端页面


🖥️ zkview：专业的工 ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/zwyywz/2022/06/15/%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%88%86%E7%B1%BB%E5%9B%BE%E7%89%87/" title="机器视觉的算法研究"><img class="post-bg" src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/t.png" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="机器视觉的算法研究"></a></div><div class="recent-post-info"><a class="article-title" href="/zwyywz/2022/06/15/%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%88%86%E7%B1%BB%E5%9B%BE%E7%89%87/" title="机器视觉的算法研究">机器视觉的算法研究</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-06-15T01:25:00.000Z" title="发表于 2022-06-15 09:25:00">2022-06-15</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-04-16T13:03:18.226Z" title="更新于 2023-04-16 21:03:18">2023-04-16</time></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/zwyywz/tags/TensorFlow/">TensorFlow</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/Python/">Python</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/">卷积神经网络</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/%E5%9B%BE%E5%83%8F%E5%88%86%E7%B1%BB/">图像分类</a></span></div><div class="content">机器视觉的算法研究本文详细解释《基于机器视觉的仿生机掌》中用到的卷积神经网络算法
机器视觉系统就是利用机器代替人眼作出各种测量和判断。它综合了光学、机械、电子、计算机软硬件等方面的技术，涉及到计算机、图像处理、模式识别、人工智能、信号处理、光机电一体化等多个领域。本章将介绍以手势分类为基础的机器视觉算法。运用机器视觉领域中最常见卷积神经网络来以分类三种“石头”、“剪刀”、“布”三种手势。本章的主要内容包括卷积神经网络概述、手势样本的采集及处理、搭建卷积神经网络、仿真结构及分析。
1、卷积神经网络概述卷积神经网络（Convolutional Neural Network, CNN）是一种前馈神经网络，对于大型图像处理有出色表现。卷积神经网络由一个或多个卷积层和顶端的全连通层组成，同时也包括关联权重和池化层（pooling layer）。卷积运算使得卷积神经网络能够利用输入数据的二维结构。在图像识别方面领域，卷积神经网络与其他深度学习结构相比能够给出更好的结果。
1.1、 卷积神经网络数学模型卷积网络是一种专门用于处理具有已知的、网格状拓扑数据的神经网络。既然被称为“卷积神经网络”，则表示 ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/zwyywz/2022/06/15/%E5%9F%BA%E4%BA%8EOpenCV%E5%92%8CC++%E8%BD%A6%E9%81%93%E7%BA%BF%E6%A3%80%E6%B5%8B/" title="基于OpenCV和C++车道线检测"><img class="post-bg" src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/weixin15937410430092-20230410105251467.png" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="基于OpenCV和C++车道线检测"></a></div><div class="recent-post-info"><a class="article-title" href="/zwyywz/2022/06/15/%E5%9F%BA%E4%BA%8EOpenCV%E5%92%8CC++%E8%BD%A6%E9%81%93%E7%BA%BF%E6%A3%80%E6%B5%8B/" title="基于OpenCV和C++车道线检测">基于OpenCV和C++车道线检测</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-06-15T01:25:00.000Z" title="发表于 2022-06-15 09:25:00">2022-06-15</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-04-16T13:03:16.086Z" title="更新于 2023-04-16 21:03:16">2023-04-16</time></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/zwyywz/tags/OpenCV/">OpenCV</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/machine-vision/">machine vision</a></span></div><div class="content">基于OpenCV和C++车道线检前言： 这篇文章主要是一种基于边缘检测的传统车道线检测算法，这种算法完全依赖于OpenCV，兼顾了轻量级和实时性。代码开源：https://github.com/Zwyywz/laneDetector

1、canny边缘检测Canny边缘检测算法主要步骤：高斯滤波、梯度计算、非极大值抑制和双阈值检测。

高斯滤波：使用高斯滤波的目的是平滑图像，滤除图像中的部分噪声（因为微分算子对噪声很敏感）
梯度计算：图像也可以计算梯度，由于数字图像是有离散的像素点的灰度值构成，所以微分运算就变成了差分，我们可以用相邻两个像素点之间的差分值表示该像素点在某个方向上灰度的变化情况。
非极大值抑制：细化边缘，梯度计算得到的边缘很粗，一条边缘中央一般很亮，两边亮度逐渐降低，可以根据这个特点去掉非局部灰度最高的“假边”，达到细化边缘的目的。
双阈值检测：减少伪边缘点非极大值抑制之后，检测到的边缘线条比较多，我们可以滤掉一些很暗的边缘，并让主要的边缘凸显出来。

OpenCV中提供了Canny边缘检测的方法，只需要调用对应的函数即可
123456789101112cv::Mat ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/zwyywz/2022/05/22/TensorRT%E6%95%99%E7%A8%8B/" title="TensorRT实战教程"><img class="post-bg" src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/FI-1.png" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="TensorRT实战教程"></a></div><div class="recent-post-info"><a class="article-title" href="/zwyywz/2022/05/22/TensorRT%E6%95%99%E7%A8%8B/" title="TensorRT实战教程">TensorRT实战教程</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-05-22T01:25:00.000Z" title="发表于 2022-05-22 09:25:00">2022-05-22</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-04-16T13:04:53.756Z" title="更新于 2023-04-16 21:04:53">2023-04-16</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/zwyywz/categories/%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/">学习笔记</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/zwyywz/tags/DeepLearning/">DeepLearning</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/C-C/">C/C++</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/TensorRT/">TensorRT</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/CUDA/">CUDA</a></span></div><div class="content">TensorRT实战教程一、开发环境搭建1.1 安装CUDANvidia-CUDA下载地址：https://developer.nvidia.com/cuda-downloads

在命令行中输入：
12wget https://developer.download.nvidia.com/compute/cuda/11.7.1/local_installers/cuda_11.7.1_515.65.01_linux.runsudo sh cuda_11.7.1_515.65.01_linux.run
1.2 安装CUDNN1.3 下载TensorRT二、推理引擎开发2.1 前期准备工作（定义通用数据结构和辅助函数）定义一些通用的数据结构来方便数据的上传和校验，比如输入输出的张量的维度、大小、名字、类型……以及一些控制流程的结构。这个文件命名为==my_common.h==，可以移植到任意工程，是只需要写一次的文件。

设置导入模型的参数

123456789101112typedef struct&#123;    char visibleCard[32];     //设置哪些ＧＰＵ ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/zwyywz/2021/06/21/YOLO%E5%8E%9F%E7%90%86%E8%AF%A6%E8%A7%A3/" title="【YOLOv5】原理详解"><img class="post-bg" src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/yolo.jpg" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="【YOLOv5】原理详解"></a></div><div class="recent-post-info"><a class="article-title" href="/zwyywz/2021/06/21/YOLO%E5%8E%9F%E7%90%86%E8%AF%A6%E8%A7%A3/" title="【YOLOv5】原理详解">【YOLOv5】原理详解</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-06-21T01:25:00.000Z" title="发表于 2021-06-21 09:25:00">2021-06-21</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-04-16T13:04:49.205Z" title="更新于 2023-04-16 21:04:49">2023-04-16</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/zwyywz/categories/%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/">学习笔记</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/zwyywz/tags/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B/">目标检测</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/YOLO/">YOLO</a></span></div><div class="content">【YOLOv5】原理详解一、目标检测的性能指标1.1、混淆矩阵（confusion matrix）

精度Precision(查准率)：评估预测准不准
召回率Recall（查全率）： 评估找的准不准

1.2 IoU(Intersection over Union)
==IoU=1 : 意味着预测边界框和地面真实边界框完全重叠。==也可以为IoU设置阈值，以确定目标检测是否有效。假设将Lou设置为0.5，在这种情况下。如果LOU &gt;=0.5，则将目标检测分类为正样本(TP)。如果LOU &lt; 0.5，则为错误检测，并将其归类为负样本(FP)。当图像中存在检测框且模型未能检测到目标时，它被认为是错误的无目标检测(FN)。正确的无目标检测(TN)：TN是我们没有预测到物体的图像的每一部分且不含有目标。
1.3 AP and mAP
AP衡量的是学习出来的模型在每个类别上的好坏
mAP衡量的是学出的模型在所有类别上的好坏。mAP就是取所有类别上AP的平均值


二、YOLOv5项目代码解析2.1 激活函数（Activations.py）激活函数相当于是一个激活单元：

激活函数使神 ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/zwyywz/2021/06/15/YOLOv5%E5%88%9D%E4%BD%93%E9%AA%8C/" title="【YOLOv5】初体验"><img class="post-bg" src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/yolo.jpg" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="【YOLOv5】初体验"></a></div><div class="recent-post-info"><a class="article-title" href="/zwyywz/2021/06/15/YOLOv5%E5%88%9D%E4%BD%93%E9%AA%8C/" title="【YOLOv5】初体验">【YOLOv5】初体验</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-06-15T01:25:00.000Z" title="发表于 2021-06-15 09:25:00">2021-06-15</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-04-16T13:04:30.018Z" title="更新于 2023-04-16 21:04:30">2023-04-16</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/zwyywz/categories/%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/">学习笔记</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/zwyywz/tags/%E7%8E%AF%E5%A2%83%E9%85%8D%E7%BD%AE/">环境配置</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/opencv/">opencv</a></span></div><div class="content">【YOLOv5】初体验1、 Yolov5简介Yolov5官方代码中，给出的目标检测网络中一共有4个版本，分别是Yolov5s、Yolov5m、Yolov5l、Yolov5x四个模型。
Yolov5的结构和Yolov4很相似，但也有一些不同。

上图即Yolov5的网络结构图，可以看出，还是分为输入端、Backbone、Neck、Prediction四个部分。
大家可能对Yolov3比较熟悉，因此大白列举它和Yolov3的一些主要的不同点，并和Yolov4进行比较。
​    （1）输入端：Mosaic数据增强、自适应锚框计算、自适应图片缩放​            （2）Backbone：Focus结构，CSP结构​            （3）Neck：FPN+PAN结构​            （4）Prediction：GIOU_Loss

Yolov5作者也是在COCO数据集上进行的测试，COCO数据集的小目标占比，因此最终的四种网络结构，性能上来说各有千秋。Yolov5s网络最小，速度最少，AP精度也最低。但如果检测的以大目标为主，追求速度，倒也是个不错的选择。其他的三种网络 ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/zwyywz/2021/06/15/%E5%9F%BA%E4%BA%8E%E6%9C%BA%E5%99%A8%E8%A7%86%E8%A7%89%E7%9A%84%E4%BB%BF%E7%94%9F%E6%9C%BA%E6%A2%B0%E6%89%8B%E6%8E%8C/" title="基于机器视觉的机械手掌"><img class="post-bg" src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/yolo.jpg" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="基于机器视觉的机械手掌"></a></div><div class="recent-post-info"><a class="article-title" href="/zwyywz/2021/06/15/%E5%9F%BA%E4%BA%8E%E6%9C%BA%E5%99%A8%E8%A7%86%E8%A7%89%E7%9A%84%E4%BB%BF%E7%94%9F%E6%9C%BA%E6%A2%B0%E6%89%8B%E6%8E%8C/" title="基于机器视觉的机械手掌">基于机器视觉的机械手掌</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-06-15T01:25:00.000Z" title="发表于 2021-06-15 09:25:00">2021-06-15</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-04-16T13:03:13.702Z" title="更新于 2023-04-16 21:03:13">2023-04-16</time></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/zwyywz/tags/DeepLearning/">DeepLearning</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/machine-vision/">machine vision</a></span></div><div class="content">基于机器视觉的机械手掌将完成机械手掌的结构设计、舵机控制板设计、嵌入式软件设计、深度学习算法设计、数字图像预处理算法设计。本组使用一种常见的带掌连杆传动机械手，在舵机控制板的设计中采用以STM32F103系列的MCU六路舵机控制器。本组对舵机控制系统进行建模，采用PID控制算法对舵机进行控制，将 PID算法部署在STM32单片机上，采用C语言实现PID算法。在深度学习算法方面，通过摄像头采集手势图像，所采集的图像先由OpenCV完成图像预处理，再将预处理完成的图片输入TensorFlow框架搭建的卷积神经网络用以分类图像，实现目标手势与模型匹配。在25ms内识别手势图片，在50ms内实现出拳动作，由此实现了用户手势和机械手掌的手势之间实时猜拳互动，达到了很好的效果。
 

一、总体技术路线机械手的总体设计包括机械结构、硬件电路、机器视觉领域的图像分类算法设计以及舵机控制算法设计四个部分。本章将对机械手掌的总体设计方案进行简要的概括。

首先第一部分是通过OpenCV进行图像预处理，这个过程是先把摄像头采集到的图像由RGB空间转换到HSV空间（HSV空间对用户来说是一种直观的颜色模型）， ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/zwyywz/2021/05/21/%E6%B2%B9%E8%80%97%E9%A2%84%E6%B5%8B/" title="汽车油耗预测实战"><img class="post-bg" src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/油耗.jpg" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="汽车油耗预测实战"></a></div><div class="recent-post-info"><a class="article-title" href="/zwyywz/2021/05/21/%E6%B2%B9%E8%80%97%E9%A2%84%E6%B5%8B/" title="汽车油耗预测实战">汽车油耗预测实战</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-05-21T01:25:00.000Z" title="发表于 2021-05-21 09:25:00">2021-05-21</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-04-16T13:03:41.010Z" title="更新于 2023-04-16 21:03:41">2023-04-16</time></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/zwyywz/tags/TensorFlow/">TensorFlow</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/">神经网络</a></span></div><div class="content">汽车油耗预测实战将利用全连接网络模型来完成汽车的效能指标MPG(Mile Per Gallon，每加仑燃油英里数)的预测问题实战
1 、数据集采用http://archive.ics.uci.edu/ml/machine-learning-databases/ 上的Auto-MPG 数据集，它记录了各种汽车效能指标与气缸数、重量、马力等其它因子的真实数据，查看数据集的前5 项，。除了产地的数字字段表示类别外，其他字段都是数值类型。对于产地地段，1 表示美国，2 表示欧洲，3 表示日本。





MPG
Cylinders
Displacement
Horsepower
Weight
Acceleration
Model Year
Origin




每加仑燃油英里
气缸数
排量
马力
重量
加速度
型号
年份




Auto MPG 数据集一共记录了398 项数据，UCI 服务器下载并读取数据集到DataFrame 对象中，代码如下：
12345678910111213def load_data():      dataset_path = keras.utils.get_fil ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/zwyywz/2021/03/31/opencv%E5%AE%89%E8%A3%85%E6%95%99%E7%A8%8B/" title="【树莓派】OpenCV3源码方式安装教程"><img class="post-bg" src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/OpenCV.png" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="【树莓派】OpenCV3源码方式安装教程"></a></div><div class="recent-post-info"><a class="article-title" href="/zwyywz/2021/03/31/opencv%E5%AE%89%E8%A3%85%E6%95%99%E7%A8%8B/" title="【树莓派】OpenCV3源码方式安装教程">【树莓派】OpenCV3源码方式安装教程</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-03-31T01:25:00.000Z" title="发表于 2021-03-31 09:25:00">2021-03-31</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-04-16T13:05:01.479Z" title="更新于 2023-04-16 21:05:01">2023-04-16</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/zwyywz/categories/%E7%8E%AF%E5%A2%83%E9%85%8D%E7%BD%AE/">环境配置</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/zwyywz/tags/%E7%8E%AF%E5%A2%83%E9%85%8D%E7%BD%AE/">环境配置</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/zwyywz/tags/opencv/">opencv</a></span></div><div class="content">【树莓派】OpenCV3源码方式安装教程一、首先我们要安装OpenCV所依赖的各种工具和图像视频库： 软件源更新,升级本地所有安装包，最新系统可以不升级，版本过高反而需要降级才能安装, 升级树莓派固件，固件比较新或者是Ubuntu则不用执行：
123sudo apt-get update sudo apt-get upgradesudo rpi-update  
然后安装构建OpenCV的相关工具，安装build-essential、cmake、git和pkg-config
1sudo apt-get install build-essential cmake git pkg-config 
之后安装常用图像工具包：安装jpeg格式图像工具包、  安装tif格式图像工具包 、安装JPEG-2000图像工具包 、安装png图像工具包：
1234sudo apt-get install libjpeg8-dev sudo apt-get install libtiff5-dev sudo apt-get install libjasper-dev sudo apt-get install  ...</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span><a class="page-number" href="/zwyywz/page/2/#content-inner">2</a><a class="extend next" rel="next" href="/zwyywz/page/2/#content-inner"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/zwyywz/img/avatar.png" onerror="this.onerror=null;this.src='/zwyywz/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">Zhouwy</div><div class="author-info__description">虽有东去大海之志，却流程缓慢，征程多艰。然涓滴之水汇成江河，已属不易，奔流向前，汇入大海之时，更会倍感自身之渺茫。</div></div><div class="card-info-data site-data is-center"><a href="/zwyywz/archives/"><div class="headline">文章</div><div class="length-num">17</div></a><a href="/zwyywz/tags/"><div class="headline">标签</div><div class="length-num">29</div></a><a href="/zwyywz/categories/"><div class="headline">分类</div><div class="length-num">5</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/zwyywz"><i class="fab fa-github"></i><span>Follow Me</span></a></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>公告</span></div><div class="announcement_content">This is my Blog</div></div><div class="sticky_layout"><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item"><a class="thumbnail" href="/zwyywz/2023/04/15/%E5%9C%A8ubuntu%E4%B8%8A%E4%BB%8E0%E5%BC%80%E5%A7%8B%E9%85%8D%E7%BD%AE%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83/" title="在Ubuntu上从0开始配置深度学习开发环境"><img src="https://cdn.educba.com/academy/wp-content/uploads/2020/01/Deep-Learning.jpg" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="在Ubuntu上从0开始配置深度学习开发环境"/></a><div class="content"><a class="title" href="/zwyywz/2023/04/15/%E5%9C%A8ubuntu%E4%B8%8A%E4%BB%8E0%E5%BC%80%E5%A7%8B%E9%85%8D%E7%BD%AE%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83/" title="在Ubuntu上从0开始配置深度学习开发环境">在Ubuntu上从0开始配置深度学习开发环境</a><time datetime="2023-04-15T01:25:00.000Z" title="发表于 2023-04-15 09:25:00">2023-04-15</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/zwyywz/2023/01/15/zkViewYolo/" title="针对zkview平台上嵌入机器视觉算法的框架"><img src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/image-20230410111343844.png" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="针对zkview平台上嵌入机器视觉算法的框架"/></a><div class="content"><a class="title" href="/zwyywz/2023/01/15/zkViewYolo/" title="针对zkview平台上嵌入机器视觉算法的框架">针对zkview平台上嵌入机器视觉算法的框架</a><time datetime="2023-01-15T01:25:00.000Z" title="发表于 2023-01-15 09:25:00">2023-01-15</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/zwyywz/2022/06/15/%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%88%86%E7%B1%BB%E5%9B%BE%E7%89%87/" title="机器视觉的算法研究"><img src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/t.png" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="机器视觉的算法研究"/></a><div class="content"><a class="title" href="/zwyywz/2022/06/15/%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%88%86%E7%B1%BB%E5%9B%BE%E7%89%87/" title="机器视觉的算法研究">机器视觉的算法研究</a><time datetime="2022-06-15T01:25:00.000Z" title="发表于 2022-06-15 09:25:00">2022-06-15</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/zwyywz/2022/06/15/%E5%9F%BA%E4%BA%8EOpenCV%E5%92%8CC++%E8%BD%A6%E9%81%93%E7%BA%BF%E6%A3%80%E6%B5%8B/" title="基于OpenCV和C++车道线检测"><img src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/weixin15937410430092-20230410105251467.png" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="基于OpenCV和C++车道线检测"/></a><div class="content"><a class="title" href="/zwyywz/2022/06/15/%E5%9F%BA%E4%BA%8EOpenCV%E5%92%8CC++%E8%BD%A6%E9%81%93%E7%BA%BF%E6%A3%80%E6%B5%8B/" title="基于OpenCV和C++车道线检测">基于OpenCV和C++车道线检测</a><time datetime="2022-06-15T01:25:00.000Z" title="发表于 2022-06-15 09:25:00">2022-06-15</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/zwyywz/2022/05/22/TensorRT%E6%95%99%E7%A8%8B/" title="TensorRT实战教程"><img src="https://blog-1300216920.cos.ap-nanjing.myqcloud.com/FI-1.png" onerror="this.onerror=null;this.src='/zwyywz/img/404.jpg'" alt="TensorRT实战教程"/></a><div class="content"><a class="title" href="/zwyywz/2022/05/22/TensorRT%E6%95%99%E7%A8%8B/" title="TensorRT实战教程">TensorRT实战教程</a><time datetime="2022-05-22T01:25:00.000Z" title="发表于 2022-05-22 09:25:00">2022-05-22</time></div></div></div></div><div class="card-widget card-categories"><div class="item-headline">
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